SAC(+LSTM)

SAC (Soft-Actor-Critic) with LSTM Agent for AutoScaling Functions

• sac_agent.py and sac_lstm_agent.py contains the agent training code
• env.py contains the gymnasium supported integrated Kubernetes/OpenFaaS environment for interaction and feedback loop
• --train & --test flags for respective train and test actions of the agent
• requirements.txt contains the project requirements

Note:

• To successfully run the agent please update the relevent placeholders marked with $PLACEHOLDER.
• Code Reference:
  

@misc{rlalgorithms,
  author = {Zihan Ding},
  title = {Popular-RL-Algorithms},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/quantumiracle/Popular-RL-Algorithms}},
}